I am an Associate Professor at Texas A&M University, Department of Electrical & Computer Engineering, and a Scientist at Brookhaven National Laboratory, Computational Science Initiative. My research is focused on machine learning and signal processing theories, models, and algorithms for various scientific applications, primarily bioinformatics and computational biology.
In this work, we presented the epidemiological model by Covid Act Now (CAN) and evaluated its performance by back-testing against historical data. For comparison, similar analyses were performed for several other COVID models and the obtained results were compared. It was found that all models generally captured the potential magnitude and directionality of the pandemic in the short term. While there are limitations to epidemiological models, understanding these limitations enables these models to be utilized as tools for “data-driven decision-making” in viral outbreaks.
The cover image is entitled “Face the Music”. The American idiom “face the music” means to accept consequences. It is thought to originate from an exhortation to face one’s stage fright. The sound waves in this image were created by superimposing Covid Act Now trend graphs representing cases, hospitalizations, ICU hospitalizations, and deaths in the United States since March 2020. This visualization represents the path of accepting the consequences of our actions and facing our fears in order to navigate the COVID pandemic: to “face the music.”
According to the National Spinal Cord Injury Statistical Center, approximately 18,000 new spinal cord injuries (SCI) occur each year in the United States. Spinal cord injuries often lead to serious constipation or incontinence, which can lead to decreased quality of life and may even be life-threatening. After a spinal cord injury, 41% of patients rated bowel dysfunction as a severe life-limiting problem.
Craig H. Neilsen Foundation recently announced that they will fund a new research project that aims to develop an optimal electrical stimulation method via reinforcement learning (RL) to help bowel dysfunction of spinal cord injury patients. In this project, Dr. Byung-Jun Yoon will collaborate with Dr. Hangue Park (PI, Texas A&M Electrical and Computer Engineering) and Dr. Cedric Geoffroy (Texas A&M College of Medicine) to develop a closed-loop stimulation scheme, which ultimately aims to improve the quality of life for SCI patients as well as their caregivers.
Recent work by Omar Maddouri, currently a Ph.D. candidate in the BioMLSP lab, on transfer learning for Bayesian error estimation has been featured in an article entitled “Doctoral student offers new insight into machine-learning error estimation”, which has been published on the Texas A&M College of Engineering website.
Our recent study on Bayesian error estimation via optimal Bayesian transfer learning has been published in Patterns, a premium open access journal from Cell Press that publishes ground-breaking original research across the full breadth of data science.
We are happy to announce that our NeurIPS 2021 paper entitled “Efficient Active Learning for Gaussian Process Classification by Error Reduction” is now available in OpenReview.net: https://openreview.net/pdf?id=UK15Hj9qX6I
We are happy to announce the availability of a new post-doc position in areas relevant to Scientific Machine Learning (SciML).
The position resides in the Applied Mathematics Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL), and the post-doctoral researcher will work with Dr. Byung-Jun Yoon and Dr. Nathan Urban on a project focused on scientific data reduction.
We invite outstanding candidates to apply for a post-doctoral research associate position in applied mathematics, machine learning, and scientific computing. This position offers a unique opportunity to conduct research in emerging interdisciplinary research problems at the intersection of applied mathematics, machine learning, and high-performance computing (HPC) with applications in diverse scientific domains of interest to BNL and the Department of Energy (DOE).
Topics of specific interest include:
optimal decision-guided data reduction
feature extraction/engineering in high-dimensional compositional workflows that involve machine learning (ML) models
Bayesian inference and uncertainty quantification in scientific ML models
learning/optimization of low-dimensional latent feature spaces for ML surrogates.
The position includes access to world-class HPC resources, such as the BNL Institutional Cluster and DOE leadership computing facilities. Access to these platforms will allow computing at scale and will ensure that the successful candidate will have the necessary resources to solve challenging DOE problems of interest.
This program provides full support for a period of two years at CSI with possible extension. Candidates must have received a doctorate (Ph.D.) in applied mathematics, statistics, computer science, or a related field (e.g., mathematics, engineering, operations research, physics) within the past five years. This post-doc position presents a unique chance to conduct interdisciplinary collaborative research in BNL programs with a highly competitive salary.
KBTX has featured Dr. Yoon and his team’s research project on scientific data reduction in their recent news.
“The team says people tend to think the more data someone has, the better it is for achieving their goal, which is not always the case. That’s why they’re working on a mechanism that can get rid of the unnecessary data without compromising what’s needed.”
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society, exploring how emerging technologies will change the way we live.
Future Tense has recently posted an article entitled “Why the Department of Energy Is Spending Millions to Get Rid of Scientific Data”, in which they featured Dr. Yoon and his team’s research project on objective-driven reduction of scientific data, recently funded by the U.S. Department of Energy (DOE). The news article can be accessed at:
“A partnership of Slate, New America, and Arizona State University, Future Tense explores how emerging technologies will change the way we live. The latest consumer gadgets are intriguing, but we focus on the longer-term transformative power of robotics, information and communication technologies, synthetic biology, augmented reality, space exploration, and other technologies. Future Tense seeks to understand the latest technological and scientific breakthroughs, and what they mean for our environment, how we relate to one another, and what it means to be human. Future Tense also examines whether technology and its development can be governed democratically and ethically. Future Tense asks these questions in daily commentary published on Slateand through public events featuring conversations with leading scientists, technologists, policymakers, and journalists.” Quoted from Future Tense website. For further info, visit: https://slate.com/future-tense